At work, one of the prompt nudges that didn't work was asking it to ask for clarifications or missing info rather than charge forward with a guess. "Sometimes d

At work, one of the prompt nudges that didn't work was asking it to ask for clarifications or missing info rather than charge forward with a guess. "Sometimes do X" instructions don't do well generally when the trigger conditions are fuzzy. (Or complex but stated in few words like "ask for missing info.") I can believe part of the miss here would be not asking the right questions--that seems to come up in some of their sample transcripts. In general at work nudging them towards finding the information they need--first search for the library to be called, etc.--has been spotty. I think tool makers are putting effort into this from their end: newer versions of IDEs seemed to do better than older ones and model makers have added things like mid-reasoning tool use that could help. The raw Internet is not full of folks transparently walking through info-gathering or introspecting about what they know or don't, so it probably falls on post-training to explicitly focus on these kinds of capabilities. I don't know what you really do. You can lean on instruction-following and give a lot of examples and descriptions of specific times to ask specific kinds of questions. You could use prompt distillation to try to turn that into better model tendencies. You could train on lots of transcripts (these days they'd probably include synthetic). You could do some kind of RL for skill at navigating situations where more info may be needed. You could treat "what info is needed and what behavior gets it?" as a type of problem to train on like math problems.